DriveIRL: Drive in Real Life with Inverse Reinforcement Learning
Tung Phan-Minh,Forbes Howington,Ting-Sheng Chu,Momchil S. Tomov,Robert E. Beaudoin,Sang Uk Lee,Nanxiang Li,Caglayan Dicle,Samuel Findler,Francisco Suarez-Ruiz,Bo Yang,Sammy Omari,Eric M. Wolff,Tung Phan-Minh,Forbes Howington,Ting-Sheng Chu,Momchil S. Tomov,Robert E. Beaudoin,Sang Uk Lee,Nanxiang Li,Caglayan Dicle,Samuel Findler,Francisco Suarez-Ruiz,Bo Yang,Sammy Omari,Eric M. Wolff
In this paper, we introduce the first published planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set of trajectory proposals and scores them with a learned model. The best trajectory is tracked by our self-driving vehicle's low-level controller. We train our trajectory scoring model on a 500+ hour real-world datas...
